The programme managers' intelligent decision maker
For some time now, the intellectual elite at the Programme Management SIG have been working on the development of an automated, interactive, system enhancing , business intelligence decision maker. This is for use in complex, agile, multi-disciplinary programmes of business change, to guide the planning, control and forward thinking of the programme board.
Taking the ideas from the impairment of autonomy and developing from the computers inability to think independently, we were successful in instilling beliefs and affiliations in version 1. The second iteration brought enhancements to perception, cognitive attention and psychological resilience using inference, causal and correlational relationships between psychosocial variables.
It is from this body of experience that we have embraced the cognitive process resulting in the selection of an action among several alternative possibilities. The four bi-polar dimensions, also known as the Myer-Briggs Type Indicator, being thinking and feeling, extroversion and introversion, judgement and perception, and sending and intuition, were embedded using neural networks.
The programme environment is modelled as Markov decision process with the probability distributions of Instantaneous cost of distribution, with the observation distribution and the Transition.
Based on the work of Hodgkin and Huxley's original model, though successful in predicting the timing and qualitative features of the action potential, it nevertheless failed to predict a number of important features such as adaptation and shunting.
We have solved this problem using a wide variety of voltage-sensitive currents, and the implications of the differing dynamics, modulations, and sensitivity of these currents is an important topic of programme forecasting.
Consider now a function of the unknown parameter: an estimator is a statistic used to estimate such function. Commonly used estimators include sample mean, unbiased sample variance and sample covariance.
A random variable which is a function of the random sample and of the unknown parameter, but whose probability distribution does not depend on the unknown parameter is called a pivotal quantity or pivot. Widely used pivots include the z-score, the chi square statistic and Student's t-value.
The estimator is said to be unbiased if its expected value is equal to the true value of the unknown parameter which is being estimated and asymptotically unbiased if its expected value converges at the limit to the true value of such parameter.
This has been proved empirically by our internal team based on thousands of iterations of testing, so demonstrating an unbiased estimator.
It will dramatically improve programme delivery and provide enhanced decision making capabilities not available elsewhere.
This level of expertise is only available through the Programme Management Specific Interest Group and their elite team of lead thinkers.